Magnetic resonance imaging using direct, continuous real-time imaging for motion compensation

ABSTRACT

Magnetic resonance imaging (MRI) uses direct, continuous, unaliased, real-time imaging for motion compensation. Unaliased, real-time two dimensional (2D) images are acquired continuously of the anatomy of interest. The images are compared to at least one template to using a correlation coefficient technique to select images corresponding to minimal motion and distortion. A spatial grid of templates can be used to cover an anatomy of interest. Multiple temporal templates can be used to create a time series of magnetic resonance (MR) images. The selected images are used to provide a high-resolution image, preferably a three dimensional (3D) image.

BACKGROUND OF THE INVENTION

The field of the invention is nuclear magnetic resonance imaging methodsand systems. More particularly, the invention relates to the reductionof image artifacts caused by patent motion during an MRI scan.

When a substance such as human tissue is subjected to a uniform magneticfield (polarizing field B₀), the individual magnetic moments of thespins in the tissue attempt to align with this polarizing field, butprecess about it in random order at their characteristic Larmorfrequency. If the substance, or tissue, is subjected to a magnetic field(excitation field B₁) which is in the x-y plane and which is near theLarmor frequency, the net aligned moment, M_(z), may be rotated, or“dipped”, into the x-y plane to produce a net transverse magnetic momentM_(t). A signal is emitted by the excited spins after the excitationsignal B₁ is terminated, this signal may be received and processed toform an image.

When utilizing these signals to produce images, magnetic field gradients(G_(x) G_(y) and G_(z)) are employed. Typically, the region to be imagedis scanned by a sequence of measurement cycles in which these gradientsvary according to the particular localization method being used. Theresulting set of received NMR signals are digitized and processed toreconstruct the image using one of many well known reconstructiontechniques.

Acquiring magnetic resonance (MR) images may require a time period ofseconds to minutes. Over this period, significant anatomical motion mayoccur—specifically, cardiac- and respiratory-induced motion. This motionproduces artifacts that may significantly degrade image quality. Anumber of different techniques have been developed in order tocompensate for the effects of this motion. These techniques attempt toeither acquire data during periods of minimal motion, or to correct forthe effects of motion when it does occur. For these techniques tocompensate for the effects of motion, the motion itself must be knownaccurately throughout the data acquisition. In the past, bellows andnavigator echoes placed on the diaphragm have been used to determinerespiratory-induced motion. A shortcoming of this approach is thatdiaphragm position may not accurately reflect respiratory-induced motionat anatomy remote from the diaphragm. For cardiac-induced motion,ECG-waveforms have been used. The problem with ECG waveforms is thatthere may be substantial variation from one cardiac cycle to the next,particularly in patient populations. Consequently, the cardiac positionmay correspondingly vary from one cycle to the next. Overall, thedrawback with previous motion compensation techniques is that they relyon indirect measures to infer the motion of the anatomy underinvestigation.

In an attempt to overcome these difficulties, there have been a numberof attempts to utilize information from the acquired data simultaneouslyfor motion compensation purposes. One technique extracts phaseinformation from the central portion of spiral interleaves to detectin-plane spatial shifts of the anatomy. A similar approach has beendeveloped using individual k-space lines. The problem with theseapproaches is that they require the assumption of rigid-body anatomicalmotion. This is a questionable assumption for respiratory-induced motionand an invalid one for cardiac-induced motion.

Recently, an adaptive averaging technique has been introduced thatcombines a real-time series of aliased, EPI images to produce a highsignal-to-noise ratio (SNR), high-resolution image. In this technique,motion compensation is accomplished by utilizing data acquired onlyduring periods of minimal motion. Such periods are identified byapplying the cross-correlation template matching technique to eachindividual image frame. The advantage of this approach is that motioncompensation is accomplished through a direct visualization of theanatomy. The disadvantage of this technique is that the resolution islimited by the amount of aliasing that is tolerable in the real-time EPIimages. Also, at present, the identification of the optimal dataacquisition periods is done in a semi-quantitative manner. Finally, thistechnique is restricted to two-dimensional (2D) imaging.

BRIEF SUMMARY OF THE INVENTION

A method and system for performing magnetic resonance imaging usesdirect, continuous, unaliased, real-time imaging for motioncompensation. This technique includes acquisition of unaliased,real-time 2D images continuously of the anatomy of interest. Periods ofminimal motion and distortion are identified by applying a correlationcoefficient (CC) technique to the real-time series. Multiple spatialtemplates can be used to increase the efficiency of the techniquewithout sacrificing anatomical coverage. Multiple temporal templates canbe used to create a time sequence series of MR images. Other MR dataacquired as part of the real-time data acquisition can be used togenerate an MR image with additional information over and above thatcontained in the real-time images including, but not limited to, highresolution and 3D information

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to the present invention;

FIG. 2 is a simplified flow chart of the method of the presentinvention;

FIGS. 3(a) to 3(e) are various photomicrographs that will be used toexplain the invention;

FIG. 4 shows a variable-density spiral acquisition according to thepresent invention;

FIG. 5 shows a variable-density echo-planer image (EPI) according to thepresent invention;

FIG. 6 shows the Fourier basis images used for multi-slice encoding;

FIG. 7 is a graph of the correlation coefficient maximum (CC_(max))values between the DC and the higher basis function images of FIG. 6;and

FIGS. 8A to 8D are photomicrographs illustrating images obtained withthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring first to FIG. 1, there is shown the major components of apreferred MRI system which incorporates the present invention. Theoperation of the system is controlled from an operator console 100 whichincludes a keyboard and control panel 102 and a display 104. The console100 communicates through a link 116 with a separate computer system 107that enables an operator to control the production and display of imageson the screen 104. The computer system 107 includes a number of moduleswhich communicate with each other through a backplane. These include animage processor module 106, a CPU module 108 and a memory module 113,known in the art as a frame buffer for storing image data arrays. Thecomputer system 107 is linked to a disk storage 111 and a tape drive 112for storage of image data and programs, and it communicates with aseparate system control 122 through a high speed serial link 115.

The system control 122 includes a set of modules connected together by abackplane. These include a CPU module 119 and a pulse generator module121 which connects to the operator console 100 through a serial link125. It is through this link 125 that the system control 122 receivescommands from the operator which indicate the scan sequence that is tobe performed. The pulse generator module 121 operates the systemcomponents to carry out the desired scan sequence. It produces datawhich indicates the timing, strength and shape of the RF pulses whichare to be produced, and the timing of and length of the data acquisitionwindow. The pulse generator module 121 connects to a set of gradientamplifiers 127, to indicate the timing and shape of the gradient pulsesto be produced during the scan. The pulse generator module 121 alsoreceives patient data from a physiological acquisition controller 129that receives signals from a number of different sensors connected tothe patient, such as ECG signals from electrodes attached to thepatient. And finally, the pulse generator module 121 connects to a scanroom interface circuit 133 which receives signals from various sensorsassociated with the condition of the patient and the magnet system. Itis also through the scan room interface circuit 133 that a patientpositioning system 134 receives commands to move the patient to thedesired position for the scan.

The gradient waveforms produced by the pulse generator module 121 areapplied to a gradient amplifier system 127 comprised of G_(x) G_(y) andG_(z) amplifiers. Each gradient amplifier excites a correspondinggradient coil in an assembly generally designated 139 to produce themagnetic field gradients used for position encoding acquired signals.The gradient coil assembly 139 forms part of a magnet assembly 141 whichincludes a polarizing magnet 140 and a whole-body RF coil 152. Atransceiver module 150 in the system control 122 produces pulses whichare amplified by an RF amplifier 151 and coupled to the RF coil 152 by atransmit/receive switch 154. The resulting signals radiated by theexcited nuclei in the patient may be sensed by the same RF coil 152 andcoupled through the transmit/receive switch 154 to a preamplifier 153.The amplified NMR signals are demodulated, filtered, and digitized inthe receiver section of the transceiver 150. The transmit/receive switch154 is controlled by a signal from the pulse generator module 121 toelectrically connect the RF amplifier 151 to the coil 152 during thetransmit mode and to connect the preamplifier 153 during the receivemode. The transmit/receive switch 154 also enables a separate RF coil(for example, a surface coil) to be used in either the transmit orreceive mode.

The NMR signals picked up by the RF coil 152 are digitized by thetransceiver module 150 and transferred to a memory module 160 in thesystem control 122. When the scan is completed and an array of rawk-space data has been acquired in the memory module 160. This rawk-space data may be rearranged into separate k-space data arrays foreach cardiac phase image (or other images) to be reconstructed, and eachof these is input to an array processor 161 which operates to Fouriertransform the data into an array of image data. This image data isconveyed through the serial link 115 to the computer system 107 where itis stored in the disk memory 111. In response to commands received fromthe operator console 100, this image data may be archived on the tapedrive 112, or it may be further processed by the image processor 106 andconveyed to the operator console 100 and presented on the display 104.For a more detailed description of the transceiver 150, reference ismade to U.S. Pat. Nos. 4,952,877 and 4,992,736 which are incorporatedherein by reference. More details about various aspects of the systemcan be found in U.S. Pat. No. 6,144,200, hereby also incorporated byreference, whereas the description that follows will concentrate onfeatures that are new.

The present invention involves a technique for acquiring MR images usingdirect, continuous visualization of the anatomy for motion compensation.The basis of this technique is the acquisition of a series of 2D,real-time images of the anatomy. These images will be used for twopurposes: First, an analysis of these images will provide theinformation used for motion compensation. Second, by combining thereal-time images together in conjunction with other data, MR images withadditional information (e.g. high resolution) can be generated.

Significantly, and as will be described in detail below, the 2D,real-time images are direct (meaning “unaliased”) images of the anatomyof interest, not some nearby anatomical feature. For example, if theanatomy of interest is a coronary artery, the 2D, real-time images willbe of the coronary artery itself, not images of another feature (e.g.,the heart outer wall) that would be used to infer the motion of thecoronary artery.

Motion compensation can be accomplished by applying the correlationcoefficient template matching technique to each 2D real-time image. Thisprocess identifies data periods where no anatomical motion or distortionhas occurred. By utilizing only data acquired during these periods, theeffects of motion and distortion can be minimized.

MR images with additional information (over and above that contained inthe 2D real-time images) can be generated by combining selectivereal-time images together with other data acquired as part of thereal-time data acquisition. The combined data may be used to generate MRimages with information including, but not limited to, higherresolution, and three-dimensional (3D) information.

Real-time images can be acquired using any k-space trajectory including,but not limited to, spirals, EPI, SMASH, and SENSE.

Turning now to FIG. 2, the motion compensation technique of the presentinvention will be described. At block 200, a series of 2D, real-timedirect images of an anatomy of interest are acquired. At block 202, oneor more templates are selected from one or more of the images. Next,block 204 involves acquiring a series of continuous, real-time images ofthe anatomy with a single outer segment and multiple inner segments of avariable-density imaging technique and/or a single encoding level of athrough-plane encoding technique. These alternate approaches for block204 will be described below.

At block 206, the similarity between each image and the template iscalculated, for example, using the correlation coefficient, At block208, a test is performed to determine if the similarity exceeds athreshold.

If block 208 determines that the similarity does not exceed thethreshold, block 210 then continues acquiring a series of continuous,real-time images of the anatomy using the same single outer segment andmultiple inner segments of a variable-density imaging technique and/orthe same single encoding level of a through-plane encoding technique asin block 206. Block 210 returns to block 206.

If block 208 determines that the similarity is at or above thethreshold, block 212 would check if a complete data set for thehigh-resolution or 3D image has been obtained. If not, control goes toblock 214 for acquisition of a series of continuous, real-time images ofthe anatomy using a different (i.e., from that in block 204) singleouter segment and multiple inner segments of a variable-density imagingtechnique and/or a different (i.e., from that in block 204) singleencoding level of a through-plane encoding technique. Block 214 leadsback to block 206.

If block 212 determines that a complete data set for the high-resolutionor 3D image has been obtained, control goes to the stop block 216.

Turning now to FIGS. 3A to 3E, further explanation of the templatematching technique will be presented using the correlation coefficient(CC) template matching technique applied to the left coronary artery(LCA). FIG. 3A is an initial image containing a template region in thewhite box. FIG. 3B is a zoomed-in or enlarged view of the template. FIG.3C illustrates how the correlation coefficient (CC) is calculatedbetween the template and different regions of a subsequent image (twolocations are indicated). The larger the CC value, the greater thesimilarity. FIG. 3D is the image of the CC at every location in theimage. The maximum value of the CC (CCmax) is the location of thetemplate in the image. FIG. 3E is a 4×2 grid of templates. Using thegrid, smaller templates can be used without sacrificing the range ofanatomical coverage. In this case, the entire left coronary artery canbe covered. The CC values for each template grid element must becalculated separately.

Individual images are analyzed using the correlation coefficient (CC)template matching technique. With this technique, an initial real-timeimage containing the anatomy of interest must be selected. From thisimage, a template consisting of a subregion of the image is extracted.The CC between the template and every location in every image in thereal-time series is calculated (FIGS. 3C and 3D). This provides twopieces of information: First, the location of the maximum value of theCC in each image (=CCmax) is the location of the template in that image.Second, the value CCmax represents the degree of similarity between theimage and template. The larger CCmax, the greater the similarity (up toa maximum of unity for identity). This information can be used to selectdata periods in which minimal motion or distortion, relative to thetemplate, has occurred. Specifically, data will be utilized only if thecorresponding image acquired during that period satisfies two criteria.First, there is limited displacement between the image and the template.Second, the CCmax value is larger than a pre-determined cutoff value

As an example, one possibility is to set the cutoff CCmax value to thevalue expected when the image and template are identical within noise.It can be shown, with a u % confidence level, that this value is givenby:

CC _(max)=tan h{tan h ⁻¹ {<CC _(max)>}+Φ_(cc)+σ_(cc) Z _(u)}

where:

<CC_(max)>=1−(σ_(n)/σ_(f))²

Φ_(cc)=<CC_(max)>/[2(N·M−1)]

σcc=1/{square root over (N·M−3)}

where Zu is the Gaussian Z-score corresponding to a u % confidencelevel, at is the standard deviation of the pixels in the template, andN, M are the x and y template dimensions.

In anatomical regions where motion is relatively rigid (e.g. the brain),the technique should perform well. However, the efficiency of thetechnique will likely be substantially reduced in areas undergoingsignificant non-rigid body motion (e.g. the heart) since only a smallpercentage of images would likely satisfy both selection criteria. Apotential method of improving the efficiency is to use smallertemplates. Over the reduced portion of anatomy covered by a smallertemplate, rigid body motion is more likely to occur. However, the entireanatomy of interest may not be covered by a smaller template. In thiscase, motion artifacts may be introduced into these areas. A remedy tothis situation is to use a grid of templates (FIG. 3E). In this case,the individual grid elements could be analyzed separately.Correspondingly, different data periods could then be used toreconstruct different parts of the final, high-resolution and/or 3Dimage.

Through the use of multiple templates spanning a region of the image, asingle, motion compensated image can be generated from a series of 2Dreal-time images. By extending this procedure to include multipletemplates in time, multiple, motion compensated MR images can begenerated. This is accomplished by using a series of templates extractedfrom a temporal sequence of real-time images.

In addition to using the real-time images for motion compensation, theycan be used in conjunction with other data to provide MR images withadditional information. The additional information may include, but notbe limited to, higher resolution, and 3D information. To accomplishthis, the fundamental assumption that is made is that additionalinformation can be acquired as part of the real-time data acquisitionwithout substantially affecting the appearance of the real-time images.In the sections below, the examples of higher resolution, and 3Dinformation are presented.

To generate high-resolution images, the approach taken here is to use ageneralized variable density k-space acquisition. With this technique,the low-resolution, unaliased, real-time images are generated from asmall number of high k-space density acquisitions of the low spatialfrequencies. During each of these acquisitions, a small amount of higherresolution information is gathered through a low k-space densityacquisition of some of the higher spatial frequencies. Since the higherspatial frequencies are acquired at a lower k-space density than the lowspatial frequencies, more data accusations will be required to generatethe full high-resolution images than the low-resolution ones. Therefore,while the unaliased, low-resolution images can e acquired in real-time,data for the full high-resolution images cannot. In fact,high-resolution images generally require a data acquisition period thatis long compared with anatomical motion. However, the effects of motioncan be minimized by applying the CC technique to the low-resolutionimages to identify the data periods where minimal motion and/ordistortion has occurred. By combining the data (both low and highspatial frequencies) from these periods, a high-resolution image can begenerated with minimal motion artifacts.

The particular nature of the variable-density acquisition isunimportant. As an example, a variable-density spiral acquisition isindicated in FIG. 4. The central (high density) part of the k-spacetrajectory is used to form the images. Another possible approach isvariable-density echo-planar image (EPI) imaging as shown in FIG. 5. Infact, other variable-density trajectories could be used.

Three-dimensional information can be generated by encoding the thirddimension information concurrently with the acquisition of thetwo-dimensional real-time images. Possible methods for encoding thethird dimension may include, but not be limited to, 3D Fourier encoding,Hadamard encoding, or multi-band encoding using an encoding matrix otherthan the Hadamard matrix, such as the Discrete Fourier transform (DFT)matrix. The major assumption that is made is that, if in-plane imagesare reconstructed in the presence of through-plane encoding, the grossstructure in the image will not be substantially affected. If thisassumption is valid, then the CC technique can be applied to thein-plane images, and motion compensation can proceed as describedpreviously. As an example, FIG. 6 represents the different basis imagesused in a DFT multi-band encoding of the heart. The Fourier basisfunctions begin with the DC component and proceed to higher slice encode(SE) orders SE1 to SE5. When added together in an appropriate manner,they will produce images of six adjacent slices (not shown).

Although the details of the anatomy differ in the different basisimages, the gross morphology of the heart is clearly visible in all. Toquantify this point, FIG. 7 indicates the CCmax values of the basisimages relative to the DC basis image. There is significant correlationbetween the different basis images indicating substantial similarity.Consequently, although slice encoding does affect the appearance of thein-plane images, enough similarity exists to apply the CC technique formotion compensation.

FIGS. 8A to 8D are the results of applying a variable-density spiralacquisition (FIG. 4) to the left coronary artery (LCA) for the purposeof high resolution. The data from the inner spiral images was used toidentify data periods of minimal motion and distortion. Using data fromthese periods, a high-resolution image FIG. 8D was constructed. FIG. 8Ais an inner spiral, low-resolution real-time image (3.4 mm resolution).FIG. 8B is a full variable-density spiral image (1.1 mm resolution).FIGS. 8C and 8C are respective zoomed-in view of the LCA in images ofFIGS. 8A and 8B. The labels in these figures are: CW=chest wall,Ao=aorta, MA=mammary artery, LAD=left anterior descending artery. Theeffectiveness of the motion compensation is demonstrated by the lack ofblurring in the high resolution image FIG. 8D.

Important features of the present invention include the use ofreal-time, unaliased 2D images for motion compensation. Other quitesignificant features of the invention in its various aspects include:the use of variable-density EPI trajectories for high-resolution MRimages; the use of through-plane encoded images for motion compensationduring a 3D acquisition; the use of the CC technique to identify imageswith minimal motion and distortion; the use of a template grid matchingtechnique for MR images; and the use of a temporal series of templatesto produce a time series of motion compensated MR images.

An method for performing magnetic resonance imaging using direct,continuous, unaliased, real-time imaging for motion compensation hasbeen described. This technique consists of the following elements:

1. Unaliased, real-time 2D images are acquired continuously of theanatomy of interest.

2. Periods of minimal motion and distortion are identified by applyingthe CC technique to the real-time series.

3. Multiple spatial templates can be used to increase the efficiency ofthe technique without sacrificing anatomical coverage.

4. Multiple temporal templates can be used to create a series of MRimages.

5. Other MR data acquired as part of the real-time data acquisition canbe used to generate an MR image with additional information over andabove that contained in the real-time images including, but not limitedto, high resolution and 3D information.

What is claimed is:
 1. A method of magnetic resonance imaging (MRI) withmotion compensation, the steps comprising: selecting at least onetemplate from real-time 2D magnetic resonance (MR) images of an anatomyof interest; subjecting the anatomy of interest to MRI to acquire aseries of unaliased real-time 2D images; selecting multiple images fromamong the series of unaliased 2D images that minimize motion-inducedartifacts by comparing images from the series of unaliased 2D images tothe at least one template; forming an output image of the anatomy ofinterest using the selected multiple images such that motion-inducedartifacts are minimized.
 2. The method of magnetic resonance imaging(MRI) with motion compensation of claim 1 wherein the step of selectingmultiple images compares images to the at least one template using acorrelation coefficient.
 3. The method of magnetic resonance imaging(MRI) with motion compensation of claim 2 wherein the series ofunaliased real-time 2D images are acquired by using variable-densityimage acquisition.
 4. The method of magnetic resonance imaging (MRI)with motion compensation of claim 1 wherein the output image is a 3Dimage.
 5. The method of magnetic resonance imaging (MRI) with motioncompensation of claim 4 wherein the selecting of at least one templatestep includes selecting a spatial grid of templates, each template beingused in the comparison.
 6. The method of magnetic resonance imaging(MRI) with motion compensation of claim 4 wherein the selecting of atleast one template step includes selecting a temporal series oftemplates, each template being used in the comparison, and furthercomprising the step of using the temporal series of templates to producea time series of motion compensated magnetic resonance images.
 7. Themethod of magnetic resonance imaging (MRI) with motion compensation ofclaim 1 wherein the selecting of at least one template step includesselecting a spatial grid of templates, each template being used in thecomparison.
 8. The method of magnetic resonance imaging (MRI) withmotion compensation of claim 6 wherein the selecting of at least onetemplate step includes selecting a temporal series of templates, eachtemplate being used in the comparison, and further comprising the stepof using the temporal series of templates to produce a time series ofmotion compensated magnetic resonance images.
 9. The method of magneticresonance imaging (MRI) with motion compensation of claim 2 wherein onlythose images selected from among the series of unaliased real-time 2-Dimages are used to form the output image.
 10. The method of magneticresonance imaging (MRI) with motion compensation of claim 1 wherein theseries of unaliased real-time images are acquired using through-planeencoding.
 11. The method of magnetic resonance imaging (MRI) with motioncompensation of claim 1 wherein the selecting of the at least onetemplate includes selecting multiple templates, the multiple templatesselected from the group consisting of: multiple spatial templates andmultiple temporal templates.